Detection of Stent Struts Relative to Side Branches

ABSTRACT

In part, the disclosure relates to methods of stent strut detection relative to a side branch region using intravascular data. In one embodiment, detecting stent struts relative to jailed side branches is performed using a scan line-based peak analysis. In one embodiment, false positive determinations relating to stent struts are analyzed using a model strut.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/162,795 filed on May 17, 2015, U.S.Provisional Patent Application No. 62/196,997 filed on Jul. 25, 2015 andU.S. Provisional Patent Application No. 62/257,185 filed on Nov. 18,2015, the disclosures of which are herein incorporated by reference intheir entirety.

FIELD

The invention relates to systems and methods for stent detection.

BACKGROUND

Interventional cardiologists incorporate a variety of diagnostic toolsduring catheterization procedures in order to plan, guide, and assesstherapies. Fluoroscopy is generally used to perform angiographic imagingof blood vessels. In turn, such blood vessel imaging is used byphysicians to diagnose, locate and treat blood vessel disease duringinterventions such as bypass surgery or stent placement. Intravascularimaging technologies such as optical coherence tomography (OCT) are alsovaluable tools that can be used in lieu of or in combination withfluoroscopy to obtain high-resolution data regarding the condition ofthe blood vessels for a given subject.

Intravascular optical coherence tomography is a catheter-based imagingmodality that uses light to peer into coronary artery walls and generateimages for study. Utilizing coherent light, interferometry, andmicro-optics, OCT can provide video-rate in-vivo tomography within adiseased vessel with micrometer level resolution. Viewing subsurfacestructures with high resolution using fiber-optic probes makes OCTespecially useful for minimally invasive imaging of internal tissues andorgans, as well as implanted medical devices such as stents.

Stents are a common intervention for treating vascular stenoses. It iscritical for a clinician to develop a personalized stent plan that iscustomized to the patient's vascular anatomy to ensure optimal outcomesin intravascular procedures. Stent planning encompasses selecting thelength, diameter, and landing zone for the stent with an intention torestore normal blood flow to the downstream tissues. However,flow-limiting stenoses are often present in the vicinity of vascularside branches. Side branches can be partially occluded or “jailed”during deployment of a stent intended to address a stenosis in the mainvessel. Since side branches are vital for carrying blood to downstreamtissues, jailing can have an undesired ischemic impact and also can leadto thrombosis. The ischemic effects of jailing are compounded whenmultiple side branches are impacted or when the occluded surface area ofa single branch is increased.

Metal stent detection methods typically detect individual stent strutsby detecting shadows cast by the struts onto the blood vessel wall,followed by detecting the location of the struts within the detectedshadows. However, struts over jailed side branches are difficult todetect via this method. Side branches appear as large shadows in imagesbecause the scan line can be perpendicular to the side branch opening.As a result, it is difficult or impossible to detect strut shadowsoverlying side branches. Consequently, jailing struts are easily missedby the shadow based detection methods.

The present disclosure addresses the need for enhanced detection ofjailing stent struts.

SUMMARY OF DISCLOSURE

Disclosed herein are systems and methods for detecting and visualizingstent struts that occlude, or jail, blood vessel side branches. Thesystems and methods disclosed herein detect jailing struts by analyzingside branches for sparse intensity peaks. In one embodiment, sparseintensity peaks include scan line intensity peaks that are surrounded bydark regions. The sparse intensity peaks can be identified on opticalcoherence tomography (OCT) scan lines. The peak corresponds to apotential strut, and the dark regions correspond to the underlying sidebranch lumen, which appears as a void. Scan lines with potential strutpeaks are analyzed to determine whether the scan lines fit an intensityprofile consistent with a jailing strut. In one embodiment, consecutivescan lines with potential strut peaks are analyzed to determine whetherthe scan lines fit an intensity profile consistent with a jailing strut.

In one embodiment, the systems and methods described herein identify aside branch and identify a potential strut at a particular locationwithin the side branch. In one embodiment, the particular location isline-offset. The system and associated side branch detection or otherassociated software module can then create a model strut at that samelocation.

In part, the disclosure relates to a method of detecting a stent strutin a representation of a blood vessel. The method includes storing, inmemory accessible by an intravascular diagnostic system, intravasculardata comprising a first group of scan lines; detecting side branches inthe intravascular data; identifying a second group of scan lines withinone or more of the detected side branches; determining a peak intensityfor each scan line in the second group of scan lines; identifying athird group of scan lines in the second group having a peak intensityless than or equal to a threshold T, wherein the third group comprisesone or more scan lines of a detected side branch that are candidates forcomprising stent strut image data; and validating the candidates toidentify one or more scan lines that comprise stent strut data.

In one embodiment, the validating step comprises determining if eachcandidate is a false positive for comprising stent strut image data. Inone embodiment, the validating step comprises comparing the candidatestent strut image data to model stent strut image data using acorrelation factor. In one embodiment, the correlation factor is alinear correlation coefficient. In one embodiment, determining if eachcandidate is a false positive for comprising stent strut image datacomprises comparing the detected candidate stent strut image data tomodel stent strut image data.

In one embodiment, after determining a peak intensity for each scanline, the method comprises a partitioning the scan lines for a sidebranch into samples. In one embodiment, the method further includes astep of clustering neighboring scan lines that are contiguous, beforevalidating against the model strut.

In one embodiment, the method further includes the step of adding avalidated strut to a list of detected struts. In one embodiment, if thenumber of samples having an intensity>peak-at-line intensity is greaterthan threshold T for a candidate strut, discarding the candidate strutor the scan line comprising the candidate strut. In one embodiment, themethod further includes determining a start frame and an end frame foreach side branch.

In part, the disclosure relates to an automatic processor-based systemfor detecting a stent strut in a representation of a blood vessel. Thesystem includes one or more memory devices; and a computing device incommunication with the memory device, wherein the memory devicecomprises instructions executable by the computing device to cause thecomputing device to: store, in memory accessible by an intravasculardiagnostic system, intravascular data comprising a first group of scanlines; detect side branches in the intravascular data; identify a secondgroup of scan lines within one or more of the detected side branches;determine a peak intensity for each scan line in the second group ofscan lines; identify a third group of scan lines in the second grouphaving a peak intensity less than or equal to a threshold T, wherein thethird group comprises one or more scan lines of a detected side branchthat are candidates for comprising stent strut image data; and validatethe candidates to identify one or more scan lines that comprise stentstrut data. Instructions to validate step comprises determining if eachcandidate is a false positive for comprising stent strut image data.

In one embodiment, the method includes instructions to validate stepcomprises comparing the candidate stent strut image data to model stentstrut image data using a correlation factor. In one embodiment, thecorrelation factor is a linear correlation coefficient. In oneembodiment, the computing device comprises further instructions to causethe computing device to determine if each candidate is a false positivefor comprising stent strut image data comprises comparing the detectedcandidate stent strut image data to model stent strut image data. In oneembodiment, after determining a peak intensity for each scan line, thecomputing device comprises further instructions to cause the computingdevice to partition the scan lines for a side branch into samples.

In one embodiment, the computing device comprises further instructionsto cause the computing device to cluster neighboring scan lines that arecontiguous, before validating against the model strut. In oneembodiment, the computing device comprises further instructions to causethe computing device to adding a validated strut to a list of detectedstruts.

In one embodiment, if the number of samples having anintensity>peak-at-line intensity is greater than threshold T for acandidate strut, discarding the candidate strut or the scan linecomprising the candidate strut. In one embodiment, the computing devicecomprises further instructions to cause the computing device todetermine a start frame and an end frame for each side branch.

BRIEF DESCRIPTION OF DRAWINGS

The figures are not necessarily to scale, emphasis instead generallybeing placed upon illustrative principles. The figures are to beconsidered illustrative in all aspects and are not intended to limit theinvention, the scope of which is defined only by the claims.

FIG. 1A is an exemplary intravascular data collection system and anassociated intravascular data collection probe and related imageprocessing, detection, and other software components according to anillustrative embodiment of the disclosure.

FIG. 1B is a cross-sectional OCT image of a stented blood vesselaccording to an illustrative embodiment of the disclosure.

FIG. 1C is a cross-sectional OCT image of a stented blood vessel thatincludes a non-oblique jailed side branch according to an illustrativeembodiment of the disclosure.

FIG. 2 is a process flow chart for detecting jailing struts in OCT imagedata according to an illustrative embodiment of the disclosure.

FIG. 3A is a graph illustrating a model strut according to anillustrative embodiment of the disclosure.

FIG. 3B is a graph illustrating detection of a true strut according toan illustrative embodiment of the disclosure.

FIG. 3C is a graph illustrating detection of a false positive strut in ablood vessel lumen according to an illustrative embodiment of thedisclosure.

FIG. 3D is a graph illustrating detection of a false positive strut inblood according to an illustrative embodiment of the disclosure.

DETAILED DESCRIPTION

The systems and methods disclosed herein describe detecting andanalyzing features of an artery using intravascular data including scanlines and images generated using scan lines or other data obtained withregard to the artery. In one embodiment, the intravascular data isanalyzed and transformed to detect metal stent struts that block, cage,or otherwise “jail” a side branch of an artery. The intravascular datacan include, for example, optical coherence tomography (OCT) orintravascular ultrasound (IVUS) data or other images of a blood vesselof interest. The intravascular data can be analyzed to identify sparseintensity peaks along each scan line—i.e., peaks that are surrounded bydark regions corresponding to a side branch which appears as a largeshadow in most cases. In many cases a side branch manifests as anopening of the tissue region in the 2-D cross sectional view. As aconsequence of this, there will be no shadows cast by the struts whichjail the side branch.

In one embodiment, a sparse peak is characterized by analyzing imagestatistics along the scan line to check if there is evidence of a brightsignal against a dark background. A threshold T or T_(s), also referredto as a naïve peak at line measurement threshold is then applied on theimage statistics to check if the scan lines are candidates for apotential metal strut. Consecutive scan lines, or portions thereof, areanalyzed to determine whether they fit an intensity profile consistentwith a metal strut in one embodiment. Other thresholds and metrics canbe used to filter and select side branch associated scan lines toidentify candidates for subsequent validation. In some implementationsfurther validation after scan line identification is not required.

FIG. 1A is a high level schematic diagram depicting a blood vessel 5,such as an artery, a data collection probe 7 and an intravascular datacollection and processing system 10. The system 10 can include forexample, an OCT, IVUS, or other intravascular imaging system. A stent 12is shown in the blood vessel 5 positioned such that is jails or blocks aside branch SB. The system 10 can include various software modulessuitable for performing side branch detection, peak detection, errorcorrection, model comparisons, lumen detection, and various otherprocesses as described herein. The system 10 can include a suitablelight source that satisfies the coherence and bandwidth requirements ofthe applications and data collection described herein. The system 10 caninclude an ultrasound imaging system. The probe 7 can include a catheter20 having a catheter portion having one or more optical fibers 15 and aprobe tip 17 disposed therein. The probe tip 17 includes a beam directorin one embodiment.

As shown, the catheter 20 is introduced into the lumen 11 such as anarterial lumen. The probe 7 can include a rotating or slidable fiber 15that directs light forward into the lumen 14 or at a directionperpendicular to the longitudinal axis of the fiber 15. As a result, inthe case of light that is directed from the side of the probe as thefiber 15 rotates, OCT data is collected with respect to the walls of theblood vessel 5. The walls of the blood vessel 5 define a lumen boundary.This lumen boundary can be detected using the distance measurementsobtained from the optical signals collected at the probe tip 17 usinglumen detection software component. Side branches and stent struts andother features can be identified in the scan lines generated during apullback through the artery by the probe. The probe 7 can include otherimaging modalities in addition to OCT such as ultrasound in oneembodiment.

As shown in FIG. 1A, the probe tip 17 is positioned in the lumen 11 suchthat it is distal to a stented region of the blood vessel 5. The probetip 17 is configured to transmit light and receive backscattered lightfrom objects, such as for example stent 12, and the wall of the bloodvessel 5. The probe tip 17 and the rest of the data collection probe 7are pulled through the lumen 11 such that the tip passes through thestented region spanning side branch SB. As shown in FIG. 1B, a probe 7is shown prior to or after insertion in a blood vessel. The probe 7 isin optical communication with an OCT system 10. The OCT system orsubsystem 10 that connects to probe 7 via an optical fiber 15 caninclude a light source such as a laser, an interferometer having asample arm and a reference arm, various optical paths, a clockgenerator, photodiodes, and other OCT system components.

In one embodiment, an optical receiver 31 such as a balanced photodiodebased system can receive light exiting the probe 7. A computing device40 such as a computer, processor, ASIC or other device can be part ofthe OCT system 10 or can be included as a separate subsystem inelectrical or optical communication with the OCT system 10. Thecomputing device 40 can include memory, storage, buses and othercomponents suitable for processing data and software 44 such as imagedata processing stages configured for side branch detection, stent strutcandidate selection or identification, stent strut validation,correlations and comparisons of stent image data stent visualization,and pullback data collection as discussed below. In one embodiment, thesoftware 44 can include a pipeline that includes various modules such asa jailed side branch/stent detection module. The module can includevarious other software modules such as a sparse peak detection module,model strut generation module, false positive testing module, and othersas described herein.

In one embodiment, the computing device 40 includes or accesses softwaremodules or programs 44, such as a side branch detection module, a lumendetection module, a stent detection module, a stent strut validationmodule, a candidate stent strut identification module and other softwaremodules. The software modules or programs 44 can include an image dataprocessing pipeline or component modules thereof and one or moregraphical user interfaces (GUI). The modules can be subsets of eachother and arranged and connected through various inputs, outputs, anddata classes.

An exemplary image processing pipeline and components thereof canconstitute one or more of the programs 44. The software modules orprograms 44 receive image data and transform such image data into twodimensional and three dimensional views of blood vessels and stents caninclude lumen detection software module, peak detection, stent detectionsoftware module, side branch detection software module and a jailed orblocked side branch module. The image data processing pipeline, itscomponents software modules and related methods and any of the methodsdescribed herein are stored in memory and executed using one or morecomputing devices such as a processor, device, or other integratedcircuit.

As shown, in FIG. 1A, a display 46 can also be part of the system 10 forshowing information 47 such as cross-sectional and longitudinal views ofa blood vessel generated using collected image data. Representations ofa stent and a side branch such as OCT or IVUS images thereof can beshown to a user via display 46. Side branch detection and stentdetection are performed prior to the display of these features and anycoding or tagging with identifying indicia that may be included in thedisplayed image. This OCT-based information 47 can be displayed usingone or more graphic user interface(s) (GUI). The images of FIGS. 1B and1C are examples of information 47 that can be displayed and interactedwith using a GUI and various input devices.

In addition, this information 47 can include, without limitation,cross-sectional scan data, longitudinal scans, diameter graphs, imagemasks, stents, areas of malapposition, lumen border, and other images orrepresentations of a blood vessel or the underlying distancemeasurements obtained using an OCT system and data collection probe. Thecomputing device 40 can also include software or programs 44, which canbe stored in one or more memory devices 45, configured to identify stentstruts and malapposition levels (such as based on a threshold andmeasured distance comparison) and other blood vessel features such aswith text, arrows, color coding, highlighting, contour lines, or othersuitable human or machine readable indicia.

Once the OCT data is obtained with a probe and stored in memory; it canbe processed to generate information 47 such as a cross-sectional, alongitudinal, and/or a three-dimensional view of the blood vessel alongthe length of the pullback region or a subset thereof. These views canbe depicted as part of a user interface as shown in FIGS. 1B and 1C andas otherwise described herein.

FIG. 1B is a cross-sectional image of a stented blood vessel obtainedusing an intravascular imaging probe, in this example, an OCT probe. Thelumen of the main blood vessel 11 is demarcated by a dashed ellipse asshown. A large side branch SB joins the main vessel at an oblique angle.The side branch lumen appears as a dark void in the OCT image data. Theside branch opening is demarcated by lines SBa and SBb. Line SBa hasbeen annotated with X shaped indicia and line SBb has been annotatedwith O shaped indicia. The sidewall of the side branch 14 is detectablein the OCT image because the side branch joins the main vessel at anoblique angle. A large strut shadow 16 is also shown in the image ofFIG. 1B. Side branch SB in the cross-sectional image of FIG. 1B cancorrespond to side branch SB in FIG. 1A in one embodiment.

Also visible in FIG. 1B are jailing stent struts 18 which were detectedin accordance with the present invention. Side branch SB is occluded bymultiple jailing struts 18. These jailing struts would be undetectableusing shadow-based strut detection methods because the jailing strutsoverlie side branch voids.

In FIG. 1B the struts might indeed be detected via the shadow method, asthe shadows are still visible against the back wall of the branch due tothe oblique angle of departure for the side branch. In contrast, FIG. 1Ccontains struts which are likely not detectable using shadow detectionbased techniques. In FIG. 1C, struts S9 and S11 are possible to detectvia shadow technique (although not guaranteed), but S10 is undetectablevia shadow techniques as there is no shadow.

Thus, there are no shadows associated with these struts in OCT imagedata. However, using the detection methods described herein, thesejailing struts are detectable. FIG. 1C is an intravascular imagegenerated using an OCT probe and an intravascular data collection andanalysis system.

The image of FIG. 1C shows an example of a non-oblique side branch, inwhich the side branch departs from the main branch at an angle close to90 degrees, and in which struts are detected relative to a side branchand otherwise as shown. User interface lines L1 and L2 are shownradiating out from the intravascular probe and bound a side branch.Stent struts S1 to S11 are shown around the lumen border. Struts S8, S9,S10 and S11 are jailing a side branch as shown. The image processing andvalidation steps described herein increase the sensitivity and accuracyof the detection of these types of jailing struts in the side branchorientation shown and others. The edges of a shadow are shown by E1 andE2.

Once detected, the struts can be displayed on a user interface, whichconveys vital information to the clinician about the precise location ofstent struts and whether adjustments may be necessary to optimize stentplacement and reduce the risk of side effects. The presence of jailingstruts over a side branch is an important input for treatment, and insome cases additional interventions can be executed to mitigate thenegative effects resulting from the jailed sidebranch. The userinterface can include cross-sectional images, L-Mode images, A-Lineimages, three dimensional renderings, or any other suitable displayformat for visualizing detected struts.

At a high level, the methods disclosed herein detect jailing struts inOCT image data by detecting bright spots that are bordered by darkregions. Stent struts, and bare metal stent struts in particular,reflect the coherent light used in OCT imaging. The methods describedherein can be used with stent struts that can be detected in anintravascular image. In one embodiment, the struts are metal struts suchas bare metal struts “BMS” for example. However, blood vessel tissues,lipid plaques, and other intravascular features also reflect coherentlight, making it difficult to distinguish struts in OCT images based onreflectivity alone. Further, as noted above, shadows cast by jailingstruts are not detectable against the backdrop of a side branch lumen.To solve this problem, an algorithm called Naïve Peak at LineMeasurement (NPLM) is provided for detecting jailing struts.

FIG. 2 is a process flow chart for detecting jailing struts in OCT imagedata. The stent strut threshold T or NPLM algorithm 100 is based on theobservation that OCT scan lines, or A-Lines, of jailing struts areessentially sparse peaks at the strut locations. That is, the scan linereflects back at different intensities, with the strut appearing brightagainst the dark backdrop of the side branch. In preferred embodiments,only scan lines beyond the catheter are analyzed because the cathetercan interfere with the detection process. The methods and systems of theinvention can include one or more of the steps described herein. Unlessotherwise required, the steps may be performed in any order. Otherthresholds T can be used in lieu of or in addition to NPLM threshold.

The first step 110 of the method 100 is to compute the peak-at-lineintensity (i.e., maximum intensity) for each scan line that correspondsto a side branch in the pullback data. Scan lines corresponding sidebranches are extracted from the original, raw image data 120 and sidebranch data 130 gathered during the imaging process. The raw image datacan be of various types and formats. For example, the raw image data canbe scan lines, 8 bit data, 16 bit data, 32 bit data, and other dataformats. The original, raw image data 120 include the OCT scan lines.The side branch data 130 include the locations of side branches in theOCT pullback. Methods, systems, and devices for detecting side branchesare known such as described in U.S. Pat. No. 8,831,321, the contents ofwhich are incorporated by reference in its entirety.

At Step 140, each side branch scan line is partitioned into a pluralityof “samples”, and the samples are subsequently analyzed for brightness.In one embodiment, the analysis uses the portion of the scan-line beyondthe imaging catheter and up to a certain depth beyond the side-branchostium (if known).

At Step 150, the samples are analyzed to count those samples withintensity above a pre-determined threshold on an image statistic alongthe scan-line. The threshold on the selected image statistic can varyfor each scan line. For example, the threshold intensity can be afunction of the maximum peak intensity for a given scan line, andsamples from that scan line can be compared against thescan-line-specific peak intensity. Alternatively, the same thresholdintensity can be used to analyze samples from different scan lines.

In one embodiment, the threshold intensity is scan-line-specific andcorresponds to the peak intensity detected at the scan-line, and samplesfrom a given scan line are screened to identify the number of sampleshaving an intensity greater than about 10% of the peak-at-line intensity(i.e., 0.1×peak-at-line) for that particular scan line. In oneembodiment, the screening of the samples to limit the result to athreshold proportional to the peak-at-line intensity generates a resultthat is equivalent to the maximum peak on that line. In one embodiment,the threshold varies for each scanline. In one embodiment, themeasurement of peak intensity varies from scanline to scanline whichyields a threshold value.

At Step 160, the number of samples calculated in Step 150 is comparedagainst an empirically determined threshold, or NPLM threshold. The NPLMthreshold is based on an upper bound on the strut blooming manifested onthe OCT image. In one embodiment, the NPLM threshold is set on a perimaging system basis. The threshold can be set empirically byestablishing a sensitivity level and adjusting the parameters of thestrut detection method accordingly. If the number of samples calculatedin Step 150 is less than or equal to the NPLM threshold, then the scanline is flagged as containing a potential strut and the processcontinues to Step 170.

As noted above, jailing struts appear as sparse peaks in the scan linesagainst the dark backdrop of side branches. The NPLM threshold tests thescan line profile to confirm the sharpness and overall width of anintensity peak(s). If too many samples exceed the threshold, then the“no” path is followed to Step 165 in which the scan line is thendiscarded as likely not containing a jailing strut or it is penalized(i.e., set aside) until/unless it is apparent that the penalized scanline is part of a continuous block of flagged scan lines.

At Step 170, neighboring flagged scan lines are clustered intocontiguous blocks. The strut region is defined as a number ofconsecutive scan lines that qualified under the NPLM threshold. Atentative final location (in terms of A-Line and offset) is alsodetermined for each strut.

At Step 180, after identifying potential struts and their locations,struts optionally can be vetted to determine whether they are truepositives or false positives. In various embodiments, the line profileof a detected strut is compared to the profile of a “model” strut at thedetected location using a linear correlation coefficient as thecomparison metric. A model strut profile is created as a sharp peak withthe same peak intensity as the detected strut and at the same locationon the scan line as the detected strut. Correlation coefficients measurethe association or similarity between two vectors or variables. Here,the correlation coefficient is defined as:

$\gamma_{xy} = \frac{\sum\limits_{i = 1}^{N}\; {\left( {x_{i} - \mu_{x}} \right)\left( {y_{i} - \mu_{y}} \right)}}{\sigma_{x}\sigma_{y}}$

-   -   where, γ_(xy) is the correlation coefficient between        measurements

x and y are the measurements, x corresponds to the detected potentialstrut and y corresponds to the model strut.

-   -   μ_(x) and μ_(y) are the respective means of those measurements,    -   and σ_(x) and σ_(y) are the respective standard deviations of        those measurements.

If the correlation coefficient is greater than an empirically determinedthreshold, which is determined based on multiple datasets andexperimental analysis, then the detected strut is deemed a true positiveand are added to a list of detected struts (Step 190). If thecorrelation coefficient is less than an empirically determinedthreshold, then the detected strut is penalized or discarded.

FIGS. 3A-D are validation graphs plotting signal intensity versus strutlocation. FIG. 3A is a graph illustrating a model strut profile. X axiscorresponds to the samples along the scan-line. Y axis corresponds tothe strut intensity. The shape of a true strut profile typically is thesame as the model strut profile and therefore, bears a high correlationwith the model strut. FIG. 3B is a graph illustrating detection of atrue strut. The peak shape of the true positive strut is similar to themodel peak. FIG. 3C is a graph illustrating detection of a falsepositive strut detected in the blood vessel lumen. In contrast to FIGS.3A and 3B, FIG. 3C shows a significant amount of signal to the right ofthe main peak. Consequently, a potential strut detected in the bloodvessel lumen with a profile shown in FIG. 3C would have a lowcorrelation to the model strut and would be discarded as a falsepositive. FIG. 3D is a graph illustrating another false positive strutcaused by blood cells within the lumen. Here too, the correlation of thepotential strut profile with respect to the model strut falls below theallowed threshold, and hence this too gets discarded as a falsepositive.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations can be used by those skilled in the computer andsoftware related fields. In one embodiment, an algorithm is here, andgenerally, conceived to be a self-consistent sequence of operationsleading to a desired result. The operations performed as methods stopsor otherwise described herein are those requiring physical manipulationsof physical quantities. Usually, though not necessarily, thesequantities take the form of electrical or magnetic signals capable ofbeing stored, transferred, combined, transformed, compared, andotherwise manipulated.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.

Embodiments of the invention may be implemented in many different forms,including, but in no way limited to, computer program logic for use witha processor (e.g., a microprocessor, microcontroller, digital signalprocessor, or general purpose computer), programmable logic for use witha programmable logic device, (e.g., a Field Programmable Gate Array(FPGA) or other PLD), discrete components, integrated circuitry (e.g.,an Application Specific Integrated Circuit (ASIC)), or any other meansincluding any combination thereof. In a typical embodiment of thepresent invention, some or all of the processing of the data collectedusing an OCT probe, an FFR probe, an angiography system, and otherimaging and subject monitoring devices and the processor-based system isimplemented as a set of computer program instructions that is convertedinto a computer executable form, stored as such in a computer readablemedium, and executed by a microprocessor under the control of anoperating system. Thus, user interface instructions, detection steps andtriggers based upon the completion of a pullback or a co-registrationrequest, for example, are transformed into processor understandableinstructions suitable for generating OCT data, detecting struts,validating struts, display detected and validated struts and performingimage procession using various and other features and embodimentsdescribed above.

Computer program logic implementing all or part of the functionalitypreviously described herein may be embodied in various forms, including,but in no way limited to, a source code form, a computer executableform, and various intermediate forms (e.g., forms generated by anassembler, compiler, linker, or locator). Source code may include aseries of computer program instructions implemented in any of variousprogramming languages (e.g., an object code, an assembly language, or ahigh-level language such as Fortran, C, C++, JAVA, or HTML) for use withvarious operating systems or operating environments. The source code maydefine and use various data structures and communication messages. Thesource code may be in a computer executable form (e.g., via aninterpreter), or the source code may be converted (e.g., via atranslator, assembler, or compiler) into a computer executable form.

The computer program may be fixed in any form (e.g., source code form,computer executable form, or an intermediate form) either permanently ortransitorily in a tangible storage medium, such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card),or other memory device. The computer program may be fixed in any form ina signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The computer program may be distributed inany form as a removable storage medium with accompanying printed orelectronic documentation (e.g., shrink-wrapped software), preloaded witha computer system (e.g., on system ROM or fixed disk), or distributedfrom a server or electronic bulletin board over the communication system(e.g., the internet or World Wide Web).

Hardware logic (including programmable logic for use with a programmablelogic device) implementing all or part of the functionality previouslydescribed herein may be designed using traditional manual methods, ormay be designed, captured, simulated, or documented electronically usingvarious tools, such as Computer Aided Design (CAD), a hardwaredescription language (e.g., VHDL or AHDL), or a PLD programming language(e.g., PALASM, ABEL, or CUPL).

Programmable logic may be fixed either permanently or transitorily in atangible storage medium, such as a semiconductor memory device (e.g., aRAM, ROM, PROM, EEPROM, or Flash-Programmable RAM), a magnetic memorydevice (e.g., a diskette or fixed disk), an optical memory device (e.g.,a CD-ROM), or other memory device. The programmable logic may be fixedin a signal that is transmittable to a computer using any of variouscommunication technologies, including, but in no way limited to, analogtechnologies, digital technologies, optical technologies, wirelesstechnologies (e.g., Bluetooth), networking technologies, andinternetworking technologies. The programmable logic may be distributedas a removable storage medium with accompanying printed or electronicdocumentation (e.g., shrink-wrapped software), preloaded with a computersystem (e.g., on system ROM or fixed disk), or distributed from a serveror electronic bulletin board over the communication system (e.g., theinternet or World Wide Web).

Various examples of suitable processing modules are discussed below inmore detail. As used herein a module refers to software, hardware, orfirmware suitable for performing a specific data processing or datatransmission task. In one embodiment, a module refers to a softwareroutine, program, or other memory resident application suitable forreceiving, transforming, routing and processing instructions, or varioustypes of data such as angiography data, OCT data, IVUS data, peakintensity, adaptive thresholds, and other information of interest asdescribed herein.

Computers and computer systems described herein may include operativelyassociated computer-readable media such as memory for storing softwareapplications used in obtaining, processing, storing and/or communicatingdata. It can be appreciated that such memory can be internal, external,remote or local with respect to its operatively associated computer orcomputer system.

Memory may also include any means for storing software or otherinstructions including, for example and without limitation, a hard disk,an optical disk, floppy disk, DVD (digital versatile disc), CD (compactdisc), memory stick, flash memory, ROM (read only memory), RAM (randomaccess memory), DRAM (dynamic random access memory), PROM (programmableROM), EEPROM (extended erasable PROM), and/or other likecomputer-readable media.

In general, computer-readable memory media applied in association withembodiments of the invention described herein may include any memorymedium capable of storing instructions executed by a programmableapparatus. Where applicable, method steps described herein may beembodied or executed as instructions stored on a computer-readablememory medium or memory media. These instructions may be softwareembodied in various programming languages such as C++, C, Java, and/or avariety of other kinds of software programming languages that may beapplied to create instructions in accordance with embodiments of theinvention.

The aspects, embodiments, features, and examples of the invention are tobe considered illustrative in all respects and are not intended to limitthe invention, the scope of which is defined only by the claims. Otherembodiments, modifications, and usages will be apparent to those skilledin the art without departing from the spirit and scope of the claimedinvention.

The use of headings and sections in the application is not meant tolimit the invention; each section can apply to any aspect, embodiment,or feature of the invention.

Throughout the application, where compositions are described as having,including, or comprising specific components, or where processes aredescribed as having, including or comprising specific process steps, itis contemplated that compositions of the present teachings also consistessentially of, or consist of, the recited components, and that theprocesses of the present teachings also consist essentially of, orconsist of, the recited process steps.

In the application, where an element or component is said to be includedin and/or selected from a list of recited elements or components, itshould be understood that the element or component can be any one of therecited elements or components and can be selected from a groupconsisting of two or more of the recited elements or components.Further, it should be understood that elements and/or features of acomposition, an apparatus, or a method described herein can be combinedin a variety of ways without departing from the spirit and scope of thepresent teachings, whether explicit or implicit herein.

The use of the terms “include,” “includes,” “including,” “have,” “has,”or “having” should be generally understood as open-ended andnon-limiting unless specifically stated otherwise.

The use of the singular herein includes the plural (and vice versa)unless specifically stated otherwise. Moreover, the singular forms “a,”“an,” and “the” include plural forms unless the context clearly dictatesotherwise. In addition, where the use of the term “about” is before aquantitative value, the present teachings also include the specificquantitative value itself, unless specifically stated otherwise. As usedherein, the term “about” refers to a ±10% variation from the nominalvalue.

It should be understood that the order of steps or order for performingcertain actions is immaterial so long as the present teachings remainoperable. Moreover, two or more steps or actions may be conductedsimultaneously.

Where a range or list of values is provided, each intervening valuebetween the upper and lower limits of that range or list of values isindividually contemplated and is encompassed within the invention as ifeach value were specifically enumerated herein. In addition, smallerranges between and including the upper and lower limits of a given rangeare contemplated and encompassed within the invention. The listing ofexemplary values or ranges is not a disclaimer of other values or rangesbetween and including the upper and lower limits of a given range.

What is claimed is:
 1. A method of detecting a stent strut in arepresentation of a blood vessel, the method comprising: storing, inmemory accessible by an intravascular diagnostic system, intravasculardata comprising a first group of scan lines; detecting side branches inthe intravascular data; identifying a second group of scan lines withinone or more of the detected side branches; determining a peak intensityfor each scan line in the second group of scan lines; identifying athird group of scan lines in the second group having a peak intensityless than or equal to a threshold T, wherein the third group comprisesone or more scan lines of a detected side branch that are candidates forcomprising stent strut image data; and validating the candidates toidentify one or more scan lines that comprise stent strut data.
 2. Themethod of claim 1, wherein the validating step comprises determining ifeach candidate is a false positive for comprising stent strut imagedata.
 3. The method of claim 1, wherein the validating step comprisescomparing the candidate stent strut image data to model stent strutimage data using a correlation factor.
 4. The method of claim 3, whereinthe correlation factor is a linear correlation coefficient.
 5. Themethod of claim 2, wherein determining if each candidate is a falsepositive for comprising stent strut image data comprises comparing thedetected candidate stent strut image data to model stent strut imagedata.
 6. The method of claim 1 wherein after determining a peakintensity for each scan line, the method comprises a partitioning thescan lines for a side branch into samples.
 7. The method of claim 1further comprising a step of clustering neighboring scan lines that arecontiguous, before validating against the model strut.
 8. The method ofclaim 1 further comprising the step of adding a validated strut to alist of detected struts.
 9. The method of claim 6 wherein if the numberof samples having an intensity>peak-at-line intensity is greater thanthreshold T for a candidate strut, discarding the candidate strut or thescan line comprising the candidate strut.
 10. The method of claim 1further comprising determining a start frame and an end frame for eachside branch.
 11. An automatic processor-based system for detecting astent strut in a representation of a blood vessel, the systemcomprising: one or more memory devices; and a computing device incommunication with the memory device, wherein the memory devicecomprises instructions executable by the computing device to cause thecomputing device to: store, in memory accessible by an intravasculardiagnostic system, intravascular data comprising a first group of scanlines; detect side branches in the intravascular data; identify a secondgroup of scan lines within one or more of the detected side branches;determine a peak intensity for each scan line in the second group ofscan lines; identify a third group of scan lines in the second grouphaving a peak intensity less than or equal to a threshold T, wherein thethird group comprises one or more scan lines of a detected side branchthat are candidates for comprising stent strut image data; and validatethe candidates to identify one or more scan lines that comprise stentstrut data.
 12. The system of claim 11 wherein instructions to validatestep comprises determining if each candidate is a false positive forcomprising stent strut image data.
 13. The system of claim 11 whereininstructions to validate step comprises comparing the candidate stentstrut image data to model stent strut image data using a correlationfactor.
 14. The system of claim 13 wherein the correlation factor is alinear correlation coefficient.
 15. The system of claim 11 wherein thecomputing device comprises further instructions to cause the computingdevice to determine if each candidate is a false positive for comprisingstent strut image data comprises comparing the detected candidate stentstrut image data to model stent strut image data.
 16. The system ofclaim 11 wherein after determining a peak intensity for each scan line,the computing device comprises further instructions to cause thecomputing device to partition the scan lines for a side branch intosamples.
 17. The system of claim 11 wherein the computing devicecomprises further instructions to cause the computing device to clusterneighboring scan lines that are contiguous, before validating againstthe model strut.
 18. The system of claim 11 wherein the computing devicecomprises further instructions to cause the computing device to adding avalidated strut to a list of detected struts.
 19. The system of claim 16wherein if the number of samples having an intensity>peak-at-lineintensity is greater than threshold T for a candidate strut, discardingthe candidate strut or the scan line comprising the candidate strut. 20.The system of claim 11 wherein the computing device comprises furtherinstructions to cause the computing device to determine a start frameand an end frame for each side branch.